ReviewJournal of neuroscience methods2021
Brain imaging-based machine learning in autism spectrum disorder: methods and applications.
Review in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 63 citations in OpenAlex.
- Construction of a small-sample brain imaging data augmentation and explainable diagnostic model for autism based on generative adversarial networks.BMC medical imaging · 2026Article
- Network analysis of serotonin CNVs shows biological convergence from genetic heterogeneity and discriminates between autism and developmental delay.Scientific reports · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Enhanced hybrid deep neural network for EEG-based schizophrenia diagnosis using functional and temporal features.Scientific reports · 2025Article
- High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals.GigaScience · 2025Article
- The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.Biological psychiatry global open science · 2025Review
- Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review.Cognitive neurodynamics · 2024Review
- Smart Autism Spectrum Disorder Learning System Based on Remote Edge Healthcare Clinics and Internet of Medical Things.Sensors (Basel, Switzerland) · 2024Article
- Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.Annual review of biomedical data science · 2024Review
- Performance reserves in brain-imaging-based phenotype prediction.Cell reports · 2024Article
- Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature.Frontiers in psychiatry · 2024Review
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- Article
- Identification of discriminative neuroimaging markers for patients on hemodialysis with insomnia: a fractional amplitude of low frequency fluctuation-based machine learning analysis.BMC psychiatry · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Hemispheric lateralization of white matter microstructure in children and its potential role in sensory processing dysfunction.Frontiers in neuroscience · 2023Article
- Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging.Frontiers in neuroinformatics · 2022Review
- Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children.Frontiers in psychiatry · 2022Article
- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent advances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.